cubble::climate_flat## # A tibble: 1,830 × 10
## id lat long elev name wmo_id date prcp tmax tmin
## <chr> <dbl> <dbl> <dbl> <chr> <dbl> <date> <dbl> <dbl> <dbl>
## 1 ASN0000… -31.9 116. 15.4 pert… 94610 2020-01-01 0 31.9 15.3
## 2 ASN0000… -31.9 116. 15.4 pert… 94610 2020-01-02 0 24.9 16.4
## 3 ASN0000… -31.9 116. 15.4 pert… 94610 2020-01-03 6 23.2 13
## 4 ASN0000… -31.9 116. 15.4 pert… 94610 2020-01-04 0 28.4 12.4
## 5 ASN0000… -31.9 116. 15.4 pert… 94610 2020-01-05 0 35.3 11.6
## # … with 1,825 more rowscubble_nested <- climate_flat |>
as_cubble(key = id, index = date, coords = c(long, lat))
cubble_nested## # cubble: id [5]: nested form
## # bbox: [115.97, -32.94, 133.55, -12.42]
## # temporal: date [date], prcp [dbl], tmax [dbl], tmin [dbl]
## id lat long elev name wmo_id ts
## <chr> <dbl> <dbl> <dbl> <chr> <dbl> <list>
## 1 ASN00009021 -31.9 116. 15.4 perth airport 94610 <tibble>
## 2 ASN00010311 -31.9 117. 179 york 94623 <tibble>
## 3 ASN00010614 -32.9 117. 338 narrogin 94627 <tibble>
## 4 ASN00014015 -12.4 131. 30.4 darwin airport 94120 <tibble>
## 5 ASN00015131 -17.6 134. 220 elliott 94236 <tibble>cubble_nested |>
mutate(rain_day = sum(ts$prcp != 0))## # cubble: id [5]: nested form
## # bbox: [115.97, -32.94, 133.55, -12.42]
## # temporal: date [date], prcp [dbl], tmax [dbl], tmin [dbl]
## id lat long elev name wmo_id ts rain_day
## <chr> <dbl> <dbl> <dbl> <chr> <dbl> <list> <int>
## 1 ASN00009021 -31.9 116. 15.4 perth airport 94610 <tibble> 104
## 2 ASN00010311 -31.9 117. 179 york 94623 <tibble> 89
## 3 ASN00010614 -32.9 117. 338 narrogin 94627 <tibble> 90
## 4 ASN00014015 -12.4 131. 30.4 darwin airpo… 94120 <tibble> 106
## 5 ASN00015131 -17.6 134. 220 elliott 94236 <tibble> 63cubble_long <- cubble_nested |> face_temporal()
cubble_long## # cubble: date, id [5]: long form
## # bbox: [115.97, -32.94, 133.55, -12.42]
## # spatial: lat [dbl], long [dbl], elev [dbl], name [chr], wmo_id
## # [dbl]
## id date prcp tmax tmin
## <chr> <date> <dbl> <dbl> <dbl>
## 1 ASN00009021 2020-01-01 0 31.9 15.3
## 2 ASN00009021 2020-01-02 0 24.9 16.4
## 3 ASN00009021 2020-01-03 6 23.2 13
## 4 ASN00009021 2020-01-04 0 28.4 12.4
## 5 ASN00009021 2020-01-05 0 35.3 11.6
## # … with 1,825 more rowscubble_back <- cubble_long |> face_spatial()
cubble_back## # cubble: id [5]: nested form
## # bbox: [115.97, -32.94, 133.55, -12.42]
## # temporal: date [date], prcp [dbl], tmax [dbl], tmin [dbl]
## id lat long elev name wmo_id ts
## <chr> <dbl> <dbl> <dbl> <chr> <dbl> <list>
## 1 ASN00009021 -31.9 116. 15.4 perth airport 94610 <tibble>
## 2 ASN00010311 -31.9 117. 179 york 94623 <tibble>
## 3 ASN00010614 -32.9 117. 338 narrogin 94627 <tibble>
## 4 ASN00014015 -12.4 131. 30.4 darwin airport 94120 <tibble>
## 5 ASN00015131 -17.6 134. 220 elliott 94236 <tibble>identical(cubble_nested, cubble_back)## [1] TRUEcubble_unfold <- cubble_long |> unfold(long, lat)
cubble_unfold## # cubble: date, id [5]: long form
## # bbox: [115.97, -32.94, 133.55, -12.42]
## # spatial: lat [dbl], long [dbl], elev [dbl], name [chr], wmo_id
## # [dbl]
## id date prcp tmax tmin long lat
## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 ASN00009021 2020-01-01 0 31.9 15.3 116. -31.9
## 2 ASN00009021 2020-01-02 0 24.9 16.4 116. -31.9
## 3 ASN00009021 2020-01-03 6 23.2 13 116. -31.9
## 4 ASN00009021 2020-01-04 0 28.4 12.4 116. -31.9
## 5 ASN00009021 2020-01-05 0 35.3 11.6 116. -31.9
## # … with 1,825 more rowsclimate_flat_ts <- climate_flat |>
tsibble::as_tsibble(key = id, index = date)
climate_flat_cb <- climate_flat_ts |>
cubble::as_cubble(coords = c(long, lat))
climate_flat_cb## # cubble: id [5]: nested form
## # bbox: [115.97, -32.94, 133.55, -12.42]
## # temporal: date [date], prcp [dbl], tmax [dbl], tmin [dbl]
## id lat long elev name wmo_id ts
## <chr> <dbl> <dbl> <dbl> <chr> <dbl> <list>
## 1 ASN00009021 -31.9 116. 15.4 perth airport 94610 <tbl_ts>
## 2 ASN00010311 -31.9 117. 179 york 94623 <tbl_ts>
## 3 ASN00010614 -32.9 117. 338 narrogin 94627 <tbl_ts>
## 4 ASN00014015 -12.4 131. 30.4 darwin airport 94120 <tbl_ts>
## 5 ASN00015131 -17.6 134. 220 elliott 94236 <tbl_ts># add station-based features in the nested form.
climate_flat_cb |>
mutate(
fabletools::features(
ts, tmax, list(tmax_mean = mean, tmax_var = var)
)
)## # cubble: id [5]: nested form
## # bbox: [115.97, -32.94, 133.55, -12.42]
## # temporal: date [date], prcp [dbl], tmax [dbl], tmin [dbl]
## id lat long elev name wmo_id ts tmax_mean tmax_var
## <chr> <dbl> <dbl> <dbl> <chr> <dbl> <list> <dbl> <dbl>
## 1 ASN00009… -31.9 116. 15.4 pert… 94610 <tbl_ts> 25.7 38.6
## 2 ASN00010… -31.9 117. 179 york 94623 <tbl_ts> 26.2 51.1
## 3 ASN00010… -32.9 117. 338 narr… 94627 <tbl_ts> 23.7 45.4
## 4 ASN00014… -12.4 131. 30.4 darw… 94120 <tbl_ts> 33.1 3.02
## 5 ASN00015… -17.6 134. 220 elli… 94236 <tbl_ts> 34.6 24.7## library(tidyverse)
## #https://www.coronavirus.vic.gov.au/victorian-coronavirus-covid-19-data
## raw <- read_csv(here::here("data/NCOV_COVID_Cases_by_LGA_Source_20220324.csv"),
## col_names = c("date", "postcode", "source", "lga"), skip = 1)
##
## covid <- raw %>%
## count(date, lga, source, postcode) %>%
## filter(date >= as.Date("2022-01-01")) %>%
## filter(source == "Contact with a confirmed case") %>%
## group_by(date, lga, source) %>%
## count() %>%
## ungroup(date) %>%
## mutate(roll_mean = zoo::rollmean(n, k = 14, fill = NA)) %>%
## tsibble::as_tsibble(key = lga, index = date) %>%
## tsibble::fill_gaps(.full = TRUE)
## save(covid, file = here::here("data/covid.rda"))load(here::here("data/covid.rda"))
lga <- strayr::read_absmap("lga2018") %>%
rename(lga = lga_name_2018) %>%
filter(state_name_2016 == "Victoria") covid %>% head(5)## # A tsibble: 5 x 5 [1D]
## # Key: lga [1]
## # Groups: lga, source [1]
## date lga source n roll_mean
## <date> <chr> <chr> <int> <dbl>
## 1 2022-01-01 Alpine (S) Contact with a confirmed case 1 NA
## 2 2022-01-02 Alpine (S) Contact with a confirmed case 2 NA
## 3 2022-01-03 Alpine (S) Contact with a confirmed case 4 NA
## 4 2022-01-04 Alpine (S) Contact with a confirmed case 4 NA
## 5 2022-01-05 Alpine (S) Contact with a confirmed case 2 NAlga %>% head(5)## Simple feature collection with 5 features and 7 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 142.3535 ymin: -38.67876 xmax: 147.3909 ymax: -36.39269
## Geodetic CRS: WGS 84
## lga_code_2018 lga state_code_2016 state_name_2016
## 132 20110 Alpine (S) 2 Victoria
## 133 20260 Ararat (RC) 2 Victoria
## 134 20570 Ballarat (C) 2 Victoria
## 135 20660 Banyule (C) 2 Victoria
## 136 20740 Bass Coast (S) 2 Victoria
## areasqkm_2018 cent_long cent_lat geometry
## 132 4788.1568 146.9742 -36.85357 MULTIPOLYGON (((146.7258 -3...
## 133 4211.1171 142.8432 -37.47271 MULTIPOLYGON (((143.1807 -3...
## 134 739.0321 143.7815 -37.49286 MULTIPOLYGON (((143.6622 -3...
## 135 62.5402 145.0851 -37.73043 MULTIPOLYGON (((145.1357 -3...
## 136 865.8095 145.5581 -38.50730 MULTIPOLYGON (((145.5207 -3...cb <- as_cubble(
list(spatial = lga, temporal = covid),
key = lga, index = date, coords = c(cent_long, cent_lat)
)## ! Some sites in the temporal table don't have corresponding spatial information## ! Some sites in the spatial table don't have corresponding temporal information## ! Use argument `output = "unmatch"` to check on the unmatched keypair <- as_cubble(
list(spatial = lga, temporal = covid),
key = lga, index = date, coords = c(cent_long, cent_lat),
output = "unmatch"
)
pair## $paired
## # A tibble: 2 × 2
## spatial temporal
## <chr> <chr>
## 1 Kingston (C) (Vic.) Kingston (C)
## 2 Latrobe (C) (Vic.) Latrobe (C)
##
## $others
## $others$temporal
## [1] "Interstate" "Overseas" "Unknown"
##
## $others$spatial
## [1] "No usual address (Vic.)"
## [2] "Migratory - Offshore - Shipping (Vic.)"lga <- lga %>%
mutate(lga = ifelse(lga == "Kingston (C) (Vic.)", "Kingston (C)", lga),
lga = ifelse(lga == "Latrobe (C) (Vic.)", "Latrobe (C)", lga)) %>%
filter(!lga %in% pair$others$spatial)
covid <- covid %>% filter(!lga %in% pair$others$temporal)
cb <- as_cubble(data = list(spatial = lga, temporal = covid),
key = lga, index = date, coords = c(cent_long, cent_lat))load(here::here("data/historical_tmax.rda"))tmax <- historical_tmax %>%
filter(between(stringr::str_sub(id, 7, 8), 46, 90))tmax <- tmax %>%
face_temporal() %>%
group_by(month = lubridate::month(date),
group = as.factor(
ifelse(lubridate::year(date) > 2015,
"2016 ~ 2020", "1971 ~ 1975"))) %>%
summarise(tmax = mean(tmax, na.rm = TRUE))tmax %>%
face_spatial() %>%
mutate(n = nrow(ts)) %>%
arrange(n) %>%
pull(n) %>%
head(10)## [1] 12 12 12 13 19 19 20 24 24 24tmax <- tmax %>%
face_spatial() %>%
filter(nrow(ts) == 24) %>%
face_temporal()cobar <- tmax %>% filter(id == "ASN00048027") %>%
ggplot(aes(x = month, y = tmax, color = group)) +
geom_line() +
scale_color_brewer(palette = "Dark2", guide = "none") +
scale_x_continuous(
breaks = seq(1, 12, 1),
labels = c("J", "F", "M", "A",
"M", "J", "J", "A",
"S", "O", "N", "D")
) +
labs(x = "", y = "Temp (C)") +
theme_bw() +
theme(aspect.ratio = 0.3)tmax <- tmax %>% unfold(long, lat)
box_df <- tmax %>% face_spatial() %>% filter(id == "ASN00048027")
nsw_vic <- ozmaps::abs_ste %>% filter(NAME %in% c("Victoria","New South Wales"))
p1 <- ggplot() +
geom_sf(data = nsw_vic, fill = "grey95",
color = "white", size=2) +
geom_glyph(
data = tmax,
aes(x_major = long, x_minor = month,
y_major = lat, y_minor = tmax,
group = interaction(id, group), color = group),
width = 1.2, height = 0.8) +
geom_rect(
data = box_df,
aes(xmin = long - 0.6, xmax = long + 0.6,
ymin = lat - 0.12, ymax = lat + 0.35),
fill = "transparent", color = "black") +
scale_color_brewer("", palette = "Dark2") +
coord_sf(xlim = c(141, 154), ylim = c(-39, -28.5)) +
ggthemes::theme_map() +
theme(legend.position = "bottom", legend.text = element_text(size = 10)) +
guides(color = guide_legend(override.aes = list(size=2)))
(p1 | cobar) +
patchwork::plot_layout(width = c(1,1)) +
plot_annotation(tag_levels = 'a')
A glyph map of the monthly maximum average temperature for weather stations in Victoria and New South Wales for the periods (1971-1975, 2016-2020). The corresponding average time series for the Cobar station are display on the top left corner. From the glyph map we can observe that the monthly trend is similar for all locations (low in the winter, high in the summer), and small increased temperatures, particularly in late summer can be seen at most stations in New South Wales.
load(here::here("data/climate_full.rda"))
climate <- climate_full %>%
filter(between(stringr::str_sub(id, 7, 8), 76, 90)) %>%
face_temporal() %>%
filter(lubridate::year(date) == 2020) %>%
face_spatial() %>%
mutate(type = "climate")
river <- river %>% mutate(type = "river")res <- match_sites(
river, climate,
temporal_by = c("Water_course_level" = "prcp"),
temporal_independent = "prcp",
temporal_n_highest = 30,
temporal_min_match = 15,
)res %>% print()## # cubble: id [8]: nested form
## # bbox: [144.52, -37.73, 146.06, -36.55]
## # temporal: date [date], matched_var [dbl]
## id name lat long type dist group ts n_match
## <chr> <chr> <dbl> <dbl> <chr> <dbl> <int> <list> <int>
## 1 405234 SEVEN CR… -36.9 146. river 6.15 5 <tibble> 21
## 2 404207 HOLLAND … -36.6 146. river 8.54 10 <tibble> 21
## 3 ASN00082042 strathbo… -36.8 146. clim… 6.15 5 <tibble> 21
## 4 ASN00082170 benalla … -36.6 146. clim… 8.54 10 <tibble> 21
## 5 230200 MARIBYRN… -37.7 145. river 6.17 6 <tibble> 19
## # … with 3 more rowsvic_map <- ozmaps::abs_ste %>%
filter(NAME == "Victoria") %>%
rmapshaper::ms_simplify()
p1 <-ggplot() +
geom_sf(data = vic_map, fill = "grey95", color = "white") +
geom_point(data = dplyr::bind_rows(river, climate),
aes(x = long, y = lat, color = type), alpha = 0.2, fill = 0.2) +
geom_point(data = res, aes(x = long, y = lat, color = type)) +
ggrepel::geom_label_repel(
data = res %>% filter(type == "river"), aes(x = long, y = lat, label = group)) +
scale_color_brewer(palette = "Dark2") +
ggthemes::theme_map() +
ggplot2::theme(legend.position = "bottom",
legend.text = element_text(size = 15),
legend.title = element_text(size = 15)) +
ggplot2::labs(x = "Longitude", y = "Latitude") +
guides(color = guide_legend(override.aes = list(size=5)))
res_long <- res %>%
face_temporal(ts) %>%
unfold(group, type) %>%
rename(prcp = matched_var) %>%
mutate(prcp = (prcp - min(prcp, na.rm = TRUE))/ (max(prcp, na.rm = TRUE) - min(prcp, na.rm = TRUE)))
p2 <- res_long %>%
ggplot(aes(x = date, y = prcp, color = type, group = id)) +
geom_line(alpha = 0.8) +
facet_wrap(vars(group)) +
scale_color_brewer(palette = "Dark2", guide = "none") +
theme_bw() +
scale_x_date(date_labels = "%b") +
labs(x = "Date", y = "Precipitation/ water level")
(p1 | p2) +
patchwork::plot_layout(guides = "collect") +
plot_annotation(tag_levels = 'a')&
theme(legend.position = "bottom") 
Weather stations and river gauges with matched pairs labelled on the map (a) and plotted across time (b). Precipitation and water level have been standardised between 0 and 1 to be displayed on the same scale. The water level reflects the increase in precipitation. The numbers (1, 5, 6, 10) indicate the group index derived from spatial matching, only those that were selectd by temporal matching are shown here.
raw <- ncdf4::nc_open(here::here("data/era5-pressure.nc"))
dt <- as_cubble(
raw, vars = c("q", "z"),
long_range = seq(-180, 180, 1), lat_range = seq(-88, 88, 1))## # prepare data/era5-pressure.nc
## library(ecmwfr)
## my_request <- list(
## product_type = "reanalysis",
## format = "netcdf",
## pressure_level = "10",
## year = "2002",
## month = c("09", "10"),
## day = c("04", "22", "26", "30"),
## time = "00:00",
## area = c(-5, -180, -90, 180),
## variable = c("specific_humidity", 'geopotential'),
## dataset_short_name = "reanalysis-era5-pressure-levels",
## target = "era5-pressure.nc"
## )
## #
## # # here you need to create an account on Climate Data Store
## # # at https://cds.climate.copernicus.eu/cdsapp#!/home) to obtain the user ID and API key
## # # then set up the with ecmwfr::wf_set_key(user, key, service = "cds")
## # # the user below is the same `user` in the setup line
## wf_request(
## user = "113339",
## request = my_request,
## transfer = TRUE,
## path = here::here("data/"))date <- c("2002-09-22", "2002-09-26", "2002-09-30", "2002-10-04") %>% as.Date()
res <- dt %>%
face_temporal() %>%
filter(lubridate::date(time) %in% date) %>%
unfold(long, lat) %>%
mutate(q = q* 10^6)
box = c(xmin = -180, ymin = -90, xmax = 180, ymax = -5)
sf::sf_use_s2(FALSE)
country <- rnaturalearth::ne_coastline("small", returnclass = "sf") %>%
sf::st_crop(box) %>%
sf::st_cast("MULTILINESTRING")
res %>%
ggplot() +
# q for specific humidity
geom_point(aes(x = long, y = lat, color = q), size = 2) +
#geom_tile(aes(x = long, y = lat, fill = q), color = "transparent") +
# z for geopotential
geom_contour(data = res, aes(x = long, y = lat, z = z),
color = "grey20", binwidth = 4000, size = 0.5) +
geom_sf(data = country , alpha = 0.5, fill = "transparent", color = "lightgreen") +
coord_sf(
crs = "+proj=stere +lat_0=-90 +lon_0=-180 +k=1 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs ", default_crs = sf::st_crs(4326), label_graticule = "EW") +
theme_bw() +
theme(legend.position = "bottom",
axis.ticks = element_blank(),
panel.border = element_blank(),
axis.text = element_blank(),
panel.grid = element_blank()) +
facet_wrap(vars(as.Date(time)), nrow = 1, shrink = TRUE)+
colorspace::scale_color_continuous_sequential("Purple-Yellow", name = "Specific humidity") +
theme(legend.text = element_text(size = 13),
legend.title = element_text(size = 13)) +
labs(x ="", y = "")
A reproduction of the second row (ERA5 data) of Figure 19 in Hersbach et al (2020) to illustrate the break-up of sourthern polar vortex in late September and early October 2002. The polar vortex, signalled by the high speicfic humidity, splits into two on 2002-09-26 and furthers split into four on 2002-10-04.
## # This chunk generates the interactive graphic and save it as figures/linking.html, where the three screencasts in the figure comes from.
## library(crosstalk)
## library(plotly)
##
## clean <- climate_full %>%
## filter(!id %in% c("ASN00067033", "ASN00072091", "ASN00059040",
## "ASN00097053", "ASN00040856", "ASN00015548")) %>%
## face_temporal() %>%
## mutate(month = lubridate::month(date)) %>%
## group_by(month) %>%
## summarise(
## tmax = mean(tmax, na.rm = TRUE),
## tmin = mean(tmin, na.rm = TRUE),
## diff = mean(tmax - tmin, na.rm = TRUE)
## ) %>%
## ungroup(month) %>%
## mutate(dummy_date = as.Date(glue::glue("2021-{month}-01"))) %>%
## face_spatial() %>%
## mutate(temp_diff_var = ts %>% pull(diff) %>% var(na.rm = TRUE))
##
## nested <- clean %>%
## SharedData$new(~id, group = "cubble")
##
## long <- clean %>%
## face_temporal() %>%
## unfold(temp_diff_var) %>%
## arrange(temp_diff_var) %>%
## SharedData$new(~id, group = "cubble")
##
## state_map <- rmapshaper::ms_simplify(ozmaps::abs_ste, keep = 2e-3)
##
## p1 <- nested %>%
## ggplot() +
## geom_sf(data = state_map, fill = "grey95", color = "white") +
## geom_point(aes(x = long, y = lat,color = temp_diff_var, label = name)) +
## colorspace::scale_color_continuous_sequential(
## "Rocket", n_interp = 7, cmax = 90, rev = TRUE,
## c2 = 40, l2= 85, c1 = 20, l1 = 30, name = "Var. temp. diff.") +
## labs(x = "Longitude", y = "Latitude") +
## coord_sf() +
## theme_bw() +
## theme(
## panel.grid.major = element_blank(),
## legend.position = "none"
## )
##
## p2 <- long %>%
## ggplot(aes(x = dummy_date, group = id,
## fill = temp_diff_var, color = temp_diff_var)) +
## geom_ribbon(aes(ymin = tmin, ymax = tmax), size = 0.1, alpha = 0.3) +
## geom_line(aes(y = tmin), alpha = 0.1) +
## geom_line(aes(y = tmax), alpha = 0.1) +
## geom_point(aes(y = tmax), size = 0.1) +
## geom_point(aes(y = tmin), size = 0.1) +
## colorspace::scale_fill_continuous_sequential(
## "Rocket", n_interp = 7, cmax = 90, rev = TRUE,
## c2 = 40, l2= 85, c1 = 20, l1 = 30, name = "Var. temp. diff.") +
## colorspace::scale_colour_continuous_sequential(
## "Rocket", n_interp = 7, cmax = 90, rev = TRUE,
## c2 = 40, l2= 85, c1 = 20, l1 = 30, name = "Var. temp. diff.") +
## scale_x_date(date_labels = "%b", date_breaks = "1 month") +
## labs(x = "Month", y = "Temperature") +
## theme_bw() +
## theme(
## panel.grid.major = element_blank(),
## legend.position = "bottom"
## )
##
## out <- bscols(
## ggplotly(p1, width = 900, height = 700, tooltip = "label") %>%
## highlight(
## on = "plotly_selected",
## off = "plotly_deselect",
## opacityDim = 0.1),
## ggplotly(p2, width = 1200, height = 700, tooltip = "label") %>%
## highlight(
## on = "plotly_selected",
## off = "plotly_deselect",
## opacityDim = 0.012),
## widths = c(5, 5)
## )
##
## htmltools::save_html(out, file = "figures/linking.html")
## 
Exploring temperature variation using linking of a map and seasonal display. Each row is a screen dump of the process. The top row shows all locations and all temperature profiles. Selecting a particular location on the map (here Mount Elizabeth) produces the plot in the second row. The maximum and minimum temperatures are shown using a ribbon. The bottom row first selects the lowest temperature in August in the seasonal display, which highlights the corresponding station on the map (Thredbo AWS). Another station, located in the Tasmania Island, is then selected to compare its temperature variation with Thredbo AWS.
sessioninfo::session_info()## ─ Session info ─────────────────────────────────────────────────────
## setting value
## version R version 4.1.2 (2021-11-01)
## os macOS Big Sur 10.16
## system x86_64, darwin17.0
## ui X11
## language (EN)
## collate en_AU.UTF-8
## ctype en_AU.UTF-8
## tz Australia/Melbourne
## date 2022-06-27
## pandoc 2.17.1.1 @ /Applications/RStudio.app/Contents/MacOS/quarto/bin/ (via rmarkdown)
##
## ─ Packages ─────────────────────────────────────────────────────────
## package * version date (UTC) lib source
## anytime 0.3.9 2020-08-27 [1] CRAN (R 4.1.0)
## assertthat 0.2.1 2019-03-21 [1] CRAN (R 4.1.0)
## backports 1.4.1 2021-12-13 [1] CRAN (R 4.1.0)
## broom 0.8.0 2022-04-13 [1] CRAN (R 4.1.2)
## bslib 0.3.1 2021-10-06 [1] CRAN (R 4.1.0)
## cellranger 1.1.0 2016-07-27 [1] CRAN (R 4.1.0)
## class 7.3-19 2021-05-03 [1] CRAN (R 4.1.2)
## classInt 0.4-3 2020-04-07 [1] CRAN (R 4.1.0)
## cli 3.3.0 2022-04-25 [1] CRAN (R 4.1.2)
## clipr 0.8.0 2022-02-22 [1] CRAN (R 4.1.2)
## codetools 0.2-18 2020-11-04 [1] CRAN (R 4.1.2)
## colorspace 2.0-3 2022-02-21 [1] CRAN (R 4.1.2)
## crayon 1.5.1 2022-03-26 [1] CRAN (R 4.1.2)
## crul 1.2.0 2021-11-22 [1] CRAN (R 4.1.0)
## cubble * 0.1.1 2022-06-02 [1] CRAN (R 4.1.2)
## curl 4.3.2 2021-06-23 [1] CRAN (R 4.1.0)
## DBI 1.1.2 2021-12-20 [1] CRAN (R 4.1.0)
## dbplyr 2.1.1 2021-04-06 [1] CRAN (R 4.1.0)
## digest 0.6.29 2021-12-01 [1] CRAN (R 4.1.0)
## dplyr * 1.0.9 2022-04-28 [1] CRAN (R 4.1.2)
## e1071 1.7-9 2021-09-16 [1] CRAN (R 4.1.0)
## ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.1.0)
## evaluate 0.15 2022-02-18 [1] CRAN (R 4.1.2)
## fansi 1.0.3 2022-03-24 [1] CRAN (R 4.1.2)
## farver 2.1.0 2021-02-28 [1] CRAN (R 4.1.0)
## fastmap 1.1.0 2021-01-25 [1] CRAN (R 4.1.0)
## forcats * 0.5.1 2021-01-27 [1] CRAN (R 4.1.0)
## foreign 0.8-81 2020-12-22 [1] CRAN (R 4.1.2)
## fs 1.5.2 2021-12-08 [1] CRAN (R 4.1.0)
## generics 0.1.2 2022-01-31 [1] CRAN (R 4.1.2)
## geojson 0.3.4 2020-06-23 [1] CRAN (R 4.1.0)
## geojsonio 0.9.4 2021-01-13 [1] CRAN (R 4.1.0)
## geojsonlint 0.4.0 2020-02-13 [1] CRAN (R 4.1.0)
## geojsonsf 2.0.2 2022-03-03 [1] CRAN (R 4.1.2)
## geosphere 1.5-14 2021-10-13 [1] CRAN (R 4.1.0)
## ggplot2 * 3.3.6 2022-05-03 [1] CRAN (R 4.1.2)
## ggrepel 0.9.1 2021-01-15 [1] CRAN (R 4.1.0)
## ggthemes 4.2.4 2021-01-20 [1] CRAN (R 4.1.0)
## glue 1.6.2 2022-02-24 [1] CRAN (R 4.1.2)
## gtable 0.3.0 2019-03-25 [1] CRAN (R 4.1.2)
## haven 2.5.0 2022-04-15 [1] CRAN (R 4.1.2)
## here 1.0.1 2020-12-13 [1] CRAN (R 4.1.0)
## highr 0.9 2021-04-16 [1] CRAN (R 4.1.0)
## hms 1.1.1 2021-09-26 [1] CRAN (R 4.1.0)
## htmltools 0.5.2 2021-08-25 [1] CRAN (R 4.1.0)
## httpcode 0.3.0 2020-04-10 [1] CRAN (R 4.1.0)
## httr 1.4.3 2022-05-04 [1] CRAN (R 4.1.2)
## isoband 0.2.5 2021-07-13 [1] CRAN (R 4.1.0)
## jqr 1.2.3 2022-03-10 [1] CRAN (R 4.1.2)
## jquerylib 0.1.4 2021-04-26 [1] CRAN (R 4.1.0)
## jsonlite 1.8.0 2022-02-22 [1] CRAN (R 4.1.2)
## jsonvalidate 1.3.2 2021-11-03 [1] CRAN (R 4.1.0)
## KernSmooth 2.23-20 2021-05-03 [1] CRAN (R 4.1.2)
## knitr 1.39 2022-04-26 [1] CRAN (R 4.1.2)
## labeling 0.4.2 2020-10-20 [1] CRAN (R 4.1.0)
## lattice 0.20-45 2021-09-22 [1] CRAN (R 4.1.2)
## lazyeval 0.2.2 2019-03-15 [1] CRAN (R 4.1.0)
## lifecycle 1.0.1 2021-09-24 [1] CRAN (R 4.1.0)
## lubridate 1.8.0 2021-10-07 [1] CRAN (R 4.1.0)
## magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.1.2)
## maptools 1.1-4 2022-04-17 [1] CRAN (R 4.1.2)
## modelr 0.1.8 2020-05-19 [1] CRAN (R 4.1.0)
## munsell 0.5.0 2018-06-12 [1] CRAN (R 4.1.0)
## ncdf4 1.19 2021-12-15 [1] CRAN (R 4.1.0)
## oz 1.0-21 2016-12-08 [1] CRAN (R 4.1.0)
## ozmaps 0.4.5 2021-08-03 [1] CRAN (R 4.1.0)
## patchwork * 1.1.1 2020-12-17 [1] CRAN (R 4.1.0)
## pillar 1.7.0 2022-02-01 [1] CRAN (R 4.1.2)
## pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.1.0)
## proxy 0.4-26 2021-06-07 [1] CRAN (R 4.1.0)
## purrr * 0.3.4 2020-04-17 [1] CRAN (R 4.1.2)
## R.cache 0.15.0 2021-04-30 [1] CRAN (R 4.1.0)
## R.methodsS3 1.8.1 2020-08-26 [1] CRAN (R 4.1.0)
## R.oo 1.24.0 2020-08-26 [1] CRAN (R 4.1.0)
## R.utils 2.11.0 2021-09-26 [1] CRAN (R 4.1.0)
## R6 2.5.1 2021-08-19 [1] CRAN (R 4.1.0)
## RColorBrewer 1.1-3 2022-04-03 [1] CRAN (R 4.1.2)
## Rcpp 1.0.8.3 2022-03-17 [1] CRAN (R 4.1.2)
## readr * 2.1.2 2022-01-30 [1] CRAN (R 4.1.2)
## readxl 1.4.0 2022-03-28 [1] CRAN (R 4.1.2)
## reprex 2.0.1 2021-08-05 [1] CRAN (R 4.1.0)
## rgdal 1.5-32 2022-05-09 [1] CRAN (R 4.1.2)
## rgeos 0.5-9 2021-12-15 [1] CRAN (R 4.1.0)
## rlang 1.0.2 2022-03-04 [1] CRAN (R 4.1.2)
## rmapshaper 0.4.6.9000 2022-05-20 [1] Github (ateucher/rmapshaper@d9ba4a2)
## rmarkdown 2.14.3 2022-06-23 [1] Github (rstudio/rmarkdown@d23e479)
## rnaturalearth 0.1.0 2017-03-21 [1] CRAN (R 4.1.0)
## rnaturalearthdata 0.1.0 2017-02-21 [1] CRAN (R 4.1.0)
## rprojroot 2.0.3 2022-04-02 [1] CRAN (R 4.1.2)
## rstudioapi 0.13 2020-11-12 [1] CRAN (R 4.1.0)
## rvest 1.0.2 2021-10-16 [1] CRAN (R 4.1.0)
## s2 1.0.7 2021-09-28 [1] CRAN (R 4.1.0)
## sass 0.4.1 2022-03-23 [1] CRAN (R 4.1.2)
## scales 1.2.0 2022-04-13 [1] CRAN (R 4.1.2)
## sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.1.0)
## sf 1.0-8 2022-06-20 [1] local
## sp 1.4-7 2022-04-20 [1] CRAN (R 4.1.2)
## strayr 0.1.6.900 2022-05-31 [1] Github (runapp-aus/strayr@c204c8a)
## stringdist 0.9.8 2021-09-09 [1] CRAN (R 4.1.0)
## stringi 1.7.6 2021-11-29 [1] CRAN (R 4.1.0)
## stringr * 1.4.0 2019-02-10 [1] CRAN (R 4.1.2)
## styler 1.7.0 2022-03-13 [1] CRAN (R 4.1.2)
## tibble * 3.1.7 2022-05-03 [1] CRAN (R 4.1.2)
## tidyr * 1.2.0 2022-02-01 [1] CRAN (R 4.1.2)
## tidyselect 1.1.2 2022-02-21 [1] CRAN (R 4.1.2)
## tidyverse * 1.3.1 2021-04-15 [1] CRAN (R 4.1.0)
## tsibble 1.1.1 2021-12-03 [1] CRAN (R 4.1.0)
## tzdb 0.3.0 2022-03-28 [1] CRAN (R 4.1.2)
## units 0.8-0 2022-02-05 [1] CRAN (R 4.1.2)
## utf8 1.2.2 2021-07-24 [1] CRAN (R 4.1.0)
## V8 4.2.0 2022-05-14 [1] CRAN (R 4.1.2)
## vctrs 0.4.1 2022-04-13 [1] CRAN (R 4.1.2)
## whisker 0.4 2019-08-28 [1] CRAN (R 4.1.0)
## withr 2.5.0 2022-03-03 [1] CRAN (R 4.1.2)
## wk 0.6.0 2022-01-03 [1] CRAN (R 4.1.2)
## xfun 0.31 2022-05-10 [1] CRAN (R 4.1.2)
## xml2 1.3.3 2021-11-30 [1] CRAN (R 4.1.0)
##
## [1] /Library/Frameworks/R.framework/Versions/4.1/Resources/library
##
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